Decisions are principled on data. The more data that is available to an entity, the better that entity (whether an individual or a business) is equipped to make a decision in order to achieve a desired outcome. Data is therefore a foundational instrument guiding personal and business dealings.
Entities look to credit reports, professional and individual reviews, social media, accreditations, and the like to facilitate decision making. One normally does not exclusively rely on the data from a single data source as that data is ordinarily relevant to a single dimension of interest. To acquire a comprehensive and holistic account, one considers data from multiple data sources. Yet, piecing together the data from the different data sources and extracting the relevant components can become overly time consuming.
For these reasons, many of the data sources provide a score to quantify and summarize the data they present. From the score, one quickly collects the gist of the data underlying the score. If more information is needed, one can then inspect the actual data used in deriving the score. However, the scores do not detract from the fundamental issue that each score is dimensional and not holistic or comprehensive. Each score is limited to the underlying data that the data source generating the score has compiled on its own. For instance, a credit score is restricted to financial risk assessment without providing insight as to the credibility, quality, and timeliness of an entity. Similarly, reviews from yelp.com are restricted to experiences that others share about an entity without providing insight as to the financial stability or historical performance of the entity.
To overcome these issues, data sources are continually seeking new data to integrate. However, data collection is a difficult task. Privacy issues limit what can be collected and entities do not freely offer data about themselves. Even when data is collected, the data should be periodically updated in order to maintain its relevancy and should be verified to ensure that the data has not been falsified or provided under false pretenses.
Some data sources strike partnerships to gain access to the data of other data sources. This involves identifying a data source that has the desired data and that is willing to share that data. Once a willing partner is found, complex negotiations ensue to set the terms and conditions for how and what data is shared. With an agreement in place, the parties can configure their interfaces, protocols, and systems to interoperate. The final hurdle involves how to meaningfully integrate and use the newly accessible partner data. Scores are generated according to complex algorithms that account for different data elements as well as the weights and values that are attributed to those data elements. In order to integrate new data, these algorithms need to be modified.
Based on the provided background, it should be evident that because of the lack of freely available data and because of the difficulty in obtaining relevant data, there is a high barrier to entry for any entity that wants to offer or utilize data as part of a service. There is also a barrier to growth for existing entities offering or utilizing data as a service since part of their growth depends on how much new data they can collect in order to expand their existing services or offer new services.
There is therefore a need to simplify data sharing between two or more parties so as to facilitate introduction and expansion of services that offer or utilize data as part of the service. In conjunction therewith, there is a need to simplify score generation for scores that are generated based on the data of two or more entities.